The DOI matches its Crossref record; 44 of the 50 references it cites were found in a registry or an index.
Distinctive Image Features from Scale-Invariant Keypoints
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Moreover, the efficiency of HF-net2 was tested and compared with several wellknown handcrafted algorithms for keypoint detection and description including Harris [4], FAST [6], SIFT [9] and ORB [8].”
From Keypoint Detection and Description through Deep Learning in Unstructured Environments (2023) · cites this paper as [9]
In the citing paper
“By including a background removal step sim-330 ilar to the WGE in [9], which makes use of a Prewitt operator in combination with a gradient filter, the authors could also demonstrate the robustness of their IP with respect to the Earth in the background.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Feature fusion was then used to synthesise the detected points into polylines which resemble parts of the spacecraft body.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Alternatively, Capuano et al. [32] introduced a new IP scheme in which three different parallel processing streams, which use the Shi-Tommasi (ST) corners detector, the HT, and the Line Segment Detector (LSD), are exploited in order to filter three sets of points and improve the robustness of the feature detection.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Furthermore, the scenarios with the Earth horizon were tackled by tuning the threshold of gradient filter to a more selective value.”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Robustness proven”
From Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft (2019) · cites this paper as [32]
In the citing paper
“Although pyramid decomposition has been widely used in image compression [8], computer vision [9], etc., its use in image hashing, as proposed in this paper, has proved to be every effective.”
From A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids (2019) · cites this paper as [9]
In the citing paper
“The latter, introduced by Lowe [2], have been shown to outperform the others [7].”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“This can be explained by the fact that they capture a substantial amount of information about the spatial intensity patterns, while at the same time being robust to small deformations or localisation errors.”
From SURF: Speeded Up Robust Features (2006) · cites this paper as [2]
In the citing paper
“While all of the above metrics rely upon gradient magnitudes, gradient orientation on its own has been widely used for feature matching (Lowe 2004), object detection (Dalal and Triggs 2005), and image registration (De Nigris et al 2012), and shown promise in robust matching under partial occlusion and view point changes.”
From 3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatch (2016) · cites this paper as Lowe 2004
In the citing paper
“Traditional methods may require complex feature matching algorithms (e.g., SIFT, SURF) or additional classifiers (e.g., SVM), whereas the MobileNetV2 network model directly utilizes features extracted by the deep learning model to perform comparison, which simplifies the process [27,28].”
From Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology (2025) · cites this paper as [27]
Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Self-citationA Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
Generalizing the Hough transform to detect arbitrary shapes
Ballard, D. H. (1981) · Pattern Recognition · doi:10.1016/0031-3203(81)90009-1 · Cited by 4,434
Robust wide-baseline stereo from maximally stable extremal regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,764
An Algorithm for Finding Best Matches in Logarithmic Expected Time
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977) · ACM Transactions on Mathematical Software · doi:10.1145/355744.355745 · Cited by 2,802
Koenderink, J. J. (1984) · Biological Cybernetics · doi:10.1007/bf00336961 · Cited by 2,452
Witkin, A. (1987) · Elsevier eBooks · doi:10.1016/b978-0-08-051581-6.50036-2 · Cited by 2,375
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Arya, S., Mount, D. M., Netanyahu, N. S., Silverman, R., & Wu, A. Y. (1998) · Journal of the ACM · doi:10.1145/293347.293348 · Cited by 2,290
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211479 · Cited by 2,046
Research works printed but not linked: 3
- Harris, C. and Stephens, M. 1988. Acombined corner and edge detector. In Fourth Alvey Vision Conference, Manchester, UK, pp. 147–151.
- Mikolajczyk, K. 2002. Detection of local features invariant to affine transformations, Ph.D. thesis, Institut National Polytechnique de Grenoble, France.
- Torr, P. 1995. Motion segmentation and outlier detection, Ph.D. Thesis, Dept. of Engineering Science, University of Oxford, UK.
Cited by
Sorted by citations
Items 32,901–33,000 of 56,201
Multi‐feature fusion of deep networks for mitosis segmentation in histological images
Zhang, Y., Chen, J., & Pan, X. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22487 · Cited by 4
Sun, S., Zeng, Z., Li, Q., & Li, T. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113126 · Cited by 4
RGBD mapping solution for low-cost robot
Beňo, P., Duchoň, F., Hubinský, P., Dekan, M., Tölgyessy, M., & Dobiš, M. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01275-0 · Cited by 4
Weakly supervised segment annotation via expectation kernel density estimation
Wang, L.-T., Li, Q., & Lu, J. (2019) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5325 · Cited by 4
Shahrokhi, M., Akoushideh, A., & Shahbahrami, A. (2021) · International Journal of Image and Graphics · doi:10.1142/s0219467822500486 · Cited by 4
On Camera Calibration with Linear Programming and Loop Constraint Linearization
Courchay, J., Dalalyan, A. S., Keriven, R., et al. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0483-6 · Cited by 4
IMU-aided SURF feature matching for relative pose estimation
Wong, D., Hayes, M., & Bainbridge-Smith, A. (2010) · doi:10.1109/ivcnz.2010.6148873 · Cited by 4
Probabilistic 3D object recognition with both positive and negative evidences
Lee, S., Lu, Z., & Kim, H. (2011) · International Conference on Computer Vision · doi:10.1109/iccv.2011.6126518 · Cited by 4
A Novel Fingerprint Image Preprocessing Algorithm
Ming, F., Huang, J., & Xu, J. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.347-350.2528 · Cited by 4
LSIFT—An improved SIFT algorithm with a new matching method
Li, L., & Long, X. (2010) · International Conference on Computer Application and System Modeling · doi:10.1109/iccasm.2010.5620607 · Cited by 4
Zhong, J., Li, M., Gruen, A., et al. (2025) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.24-00156r3 · Cited by 4
Gao, J., Wu, J., Zhao, X., et al. (2023) · The Visual Computer · doi:10.1007/s00371-023-03065-9 · Cited by 4
Multi-objective sensor-based replanning for a car-like robot
Grady, D. K., Moll, M., Hegde, C., et al. (2012) · IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) · doi:10.1109/ssrr.2012.6523898 · Cited by 4
Wary, A., & Neelima, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08412-4 · Cited by 4
Weakly supervised object-aware convolutional neural networks for semantic feature matching
Lyu, W., Chen, L., Zhou, Z., & Wu, W. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.052 · Cited by 4
Resin comparison for serial block face scanning volume electron microscopy
Borghgraef, P., Kremer, A., Bruyne, M. D., Guérin, C. J., & Lippens, S. (2023) · Methods in cell biology · doi:10.1016/bs.mcb.2023.01.011 · Cited by 5
New Scheme of Image Matching for The Power Transmission Line Icing
Nusantika, N. R., Xiao, J., & Hu, X. (2022) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea54703.2022.10006260 · Cited by 4
SAR Sea Ice Type Classification and Drift Retrieval in the Arctic
Zakhvatkina, N. Y., Demchev, D., Sandven, S., Волков, В. А., & Komarov, A. S. (2019) · Springer polar sciences · doi:10.1007/978-3-030-21301-5_6 · Cited by 4
Research on 2D representation method of wireless Micro-Ball endoscopic images
Wang, D., Xie, X., Li, G., et al. (2012) · doi:10.1109/embc.2012.6346138 · Cited by 4
A discriminative algorithm for indoor place recognition based on clustering of features and images
Wang, K., Long, X., Li, R., et al. (2017) · Machine Intelligence Research · doi:10.1007/s11633-017-1081-z · Cited by 4
Simultaneous place and object recognition using collaborative context information
Kim, S., & Kweon, I. S. (2008) · Image and Vision Computing · doi:10.1016/j.imavis.2008.07.010 · Cited by 4
RGB-depth Fusion Framework for Object Detection in Autonomous Vehicles
Farahnakian, F., & Heikkonen, J. (2020) · International Conference on Signal Processing and Communication Systems · doi:10.1109/icspcs50536.2020.9310031 · Cited by 4
Distributed Kd-Trees for Ultra Large Scale Object Recognition
Aly, M., Munich, M. E., & Perona, P. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.40 · Cited by 4
Combining local descriptors for 3D object recognition and categorization
Salgian, A. S. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761182 · Cited by 4
STRATEGIES FOR QUALITY-AWARE VIDEO CONTENT ANALYTICS
Reibman, A. R. (2018) · IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) · doi:10.1109/ssiai.2018.8470354 · Cited by 4
Spherical Video Stabilization by Estimating Rotation from Dense Optical Flow Fields
Pathak, S., Moro, A., Fujii, H., et al. (2017) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2017.p0566 · Cited by 4
Digital Image Forensics with Statistical Analysis
Pan, X. (2015) · doi:10.1002/9781118705773.ch13 · Cited by 4
Effectiveness of optimal incremental multi-step nearest neighbor search
Zhang, M., Alhajj, R., & Rokne, J. G. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.02.141 · Cited by 4
An Analysis-Oriented ROI Based Coding Approach on Surveillance Video Data
Liao, L., Hu, R., Xiao, J., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48896-7_42 · Cited by 4
Texture Descriptors Based on Dijkstra's Algorithm for Medical Image Analysis
Ghidoni, S., Nanni, L., Brahnam, S., et al. (2014) · Studies in health technology and informatics · doi:10.3233/978-1-61499-474-9-74 · Cited by 4
DiffGlue: Diffusion-Aided Image Feature Matching
Zhang, S., & Ma, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681069 · Cited by 10
“A mature and effective pipeline of image matching begins with detecting and describing feature points in both images, where great efforts have been spent on applicable detectors and descriptors [13, 33, 65, 79].”
A Novel Harris Feature Detection-Based Registration for Remote Sensing Image
Wang, Y., Lai, H., Ma, H., et al. (2020) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-020-01151-2 · Cited by 4
Zhang, S., Tao, Z., & Lin, S. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06635-7 · Cited by 4
VisHash: visual similarity preserving image hashing for diagram retrieval
Oyen, D., Kucer, M., & Wohlberg, B. E. (2021) · SPIE Applications of Machine Learning · doi:10.1117/12.2594720 · Cited by 4
RETRACTED: An enhanced approach for object detection using wavelet based neural network
Krishnaveni, G., Bhavani, B. L., & Lakshmi, N. V. S. K. V. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1228/1/012032 · Cited by 4
RetractedColor image retrieval based on the hypergraph and the fusion of two descriptors
Yu, S., Niu, D., Zhao, X., et al. (2017) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei.2017.8301985 · Cited by 4
Digital three-dimensional reconstruction based on integral imaging
Li, C., Chen, Q., Hua, H., Mao, C., & Shao, A. (2015) · Optical Review · doi:10.1007/s10043-015-0074-9 · Cited by 4
A real-time multi-scale 2D Gaussian filter based on FPGA
Luo, H., Gai, X., Chang, Z., et al. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2072284 · Cited by 4
Orthophoto and DEM Generation Using Low Specification UAV Images from Different Altitudes
Lee, K., & Lee, W. H. (2016) · Journal of the Korean Society of Surveying Geodesy Photogrammetry and Cartography · doi:10.7848/ksgpc.2016.34.5.535 · Cited by 4
Medical Image Classification Using Multi-Vocabulary
Mueen, A., ALGhamdi, A. S. A.-M., Zafar, B. A., et al. (2014) · Asian Journal of Applied Sciences · doi:10.3923/ajaps.2015.71.78 · Cited by 4
Uncalibrated Image-based Visual Servoing Based on Joint Space and Image Moment
Wu, D., Zhong, X., Zhang, X., et al. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8483954 · Cited by 4
Kim, S., & Kweon, I. S. (2006) · Lecture notes in computer science · doi:10.1007/11612704_96 · Cited by 4
Local shape features for object recognition
Heisele, B., & Rocha, C. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761194 · Cited by 4
Machine learning technique for object detection based on SURF feature
Ahsan, A. M., & Mohamad, D. (2017) · International Journal of Computational Vision and Robotics · doi:10.1504/ijcvr.2017.081232 · Cited by 4
Adaptive Co-Weighting Deep Convolutional Features for Object Retrieval
Wang, J., Zhu, J., Pang, S., et al. (2018) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486610 · Cited by 4
The Challenges and Solutions for Building an Integrated Video Analytics Platform
Mathew, G. (2017) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2017.53 · Cited by 4
Intelligent Interactive Applications for Museum Visits
Özden, K. E., Ünay, D., Inan, H., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13695-0_55 · Cited by 4
Analyze the learner's concentration using detection of facial feature points
Cha, S., & Kim, W. (2015) · Advanced science and technology letters · doi:10.14257/astl.2015.92.15 · Cited by 4
GAMFlow: Global Attention-Based Flow Model for Anomaly Detection and Localization
Zhang, F., Yan, R., Li, J., He, J., & Fang, C. (2023) · IEEE Access · doi:10.1109/access.2023.3326753 · Cited by 4
Invariant properties of a locally salient dither pattern with a spatial-chromatic histogram
Bandara, A. M. R. R., Ranathunga, L., & Abdullah, N. A. (2013) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciinfs.2013.6732000 · Cited by 4
A Research Platform for Embodied Visual Object Recognition
Wallenberg, M., & Forssén, P.-E. (2010) · Cited by 4
Mao, H., Ma, Y., Zhang, D., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3467256 · Cited by 4
Pedrosa, G. V., Rezende, S. O., & Traina, A. J. M. (2012) · Brazilian Symposium on Multimedia and the Web · doi:10.1145/2382636.2382668 · Cited by 4
Kaneko, A., Komatsu, A., Itoh, T., et al. (2020) · Visual Computing for Industry Biomedicine and Art · doi:10.1186/s42492-019-0040-7 · Cited by 4
Automatic object detection employing viewing angle histogram for range images
Chen, L.-C., Nguyen, X.-L., & Lin, S.-T. (2012) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim.2012.6266019 · Cited by 4
Localization and pose estimation of textureless objects for autonomous exploration missions
Wojke, N., Neuhaus, F., & Paulus, D. W. R. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532569 · Cited by 4
Diffusion Tensor Imaging retrieval for Alzheimer's disease diagnosis
Ahmed, O. B., Benois‐Pineau, J., Michèle Allard, A., et al. (2014) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2014.6849831 · Cited by 4
3D city models completion by fusing lidar and image data
Γραμματικόπουλος, Λ., Kalisperakis, I., Petsa, E., et al. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2184722 · Cited by 4
A robust image registration algorithm used for panoramic image mosaic
Bo, G., Cao, J., Zhou, Z., et al. (2012) · International Conference on Image Analysis and Signal Processing · doi:10.1109/iasp.2012.6425046 · Cited by 4
Modeling Local Interest Points for Semantic Detection and Video Search at TRECVID 2006.
Jiang, Y., Wei, X.-Y., Ngo, C., et al. (2006) · Cited by 4
Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking
Zhang, X., Guan, N., Tao, D., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124685 · Cited by 4
Multiple hypotheses data association propagation for robust monocular-based SLAM algorithms
Soto-Alvarez, M., & Honkamaa, P. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854865 · Cited by 4
Multi-objective Mapping and Path Planning using Visual SLAM and Object Detection
Woo, A. (2019) · UWSpace (University of Waterloo) · Cited by 4
Determinant of homography-matrix-based multiple-object recognition
Bangalore, N. M., Kiran, M., & Suryaprakash, A. R. K. (2013) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2003767 · Cited by 4
Srinivasa, R. J., Dodson, S., Seo, K., et al. (2021) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl52503.2021.00026 · Cited by 4
A frame-based probabilistic local verification method for robust correspondence
Shen, L. C., Xu, Z., Zhu, J., et al. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.08.015 · Cited by 4
Localizing Assets in an Indoor Environment Using Sensor Fusion
Murali, R., Nachimuthu, D., SenthilKumar, D. V., et al. (2018) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2018.8554754 · Cited by 4
Landmark Recognition: State-of-the-Art Methods in a Large-Scale Scenario?
Rischka, M., & Conrad, S. (2015) · Cited by 4
Machine Learning Approaches to Human Body Shape Analysis
Piccirilli, M. (2018) · The Research Repository @ WVU (West Virginia University) · doi:10.33915/etd.6417 · Cited by 4
Appearance and map-based global localization using laser reflectivity
Zhang, D., Kurazume, R., Iwashita, Y., & Hasegawa, T. (2011) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2011.6181420 · Cited by 4
Content-Based Image Detection of Semantic Similarity
Wu, C. (2010) · International Workshop on Education Technology and Computer Science · doi:10.1109/etcs.2010.404 · Cited by 4
Zhao, S., Yin, Y., Wang, J., et al. (2025) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2025.109241 · Cited by 4
Real-to-Synthetic Feature Transform for Illumination Invariant Camera Localization
Shoman, S., Mashita, T., Plopski, A., Ratsamee, P., & Uranishi, Y. (2020) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2020.3041617 · Cited by 4
Li, M., Hu, Q., Wang, Y., & Dong, W. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.08.025 · Cited by 4
Warping trajectories for video synchronization
Shankar, S., Lasenby, J., & Kokaram, A. C. (2013) · doi:10.1145/2510650.2510654 · Cited by 4
Extraction of query term-related visual phrases for news video retrieval using mutual information
Yeh, J.-B., & Wu, C. (2009) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2009.5117852 · Cited by 4
Initiating GrabCut by color difference for automatic foreground extraction of passport imagery
Sangüesa, A. A., Jørgensen, N. K., Larsen, C. A., et al. (2016) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2016.7820964 · Cited by 4
An Insight into Content-Based Image Retrieval Techniques, Datasets, and Evaluation Metrics
Bhat, J. I., Yousuf, R., Jeelani, Z., & Bhat, O. (2024) · Engergy systems in electrical engineering · doi:10.1007/978-981-99-8771-9_8 · Cited by 4
Vision-IMU based collaborative control of a blind UAV
Hoogervorst, R., Stramigioli, S., Wopereis, H. W., et al. (2015) · Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS) · doi:10.1109/red-uas.2015.7440990 · Cited by 4
Efficient Image Retrieval Using Hierarchical K-Means Clustering
Park, D., & Hwang, Y. (2024) · Sensors · doi:10.3390/s24082401 · Cited by 4
“A typical local feature extractor, SIFT [25], uses image pyramids to extract size and rotation-invariant features.”
MethodEFFICIENT ORIENTATION AND CALIBRATION OF LARGE AERIAL BLOCKS OF MULTI-CAMERA PLATFORMS
Karel, W., Camillo Ressl, A., & Pfeifer, N. (2016) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xli-b1-199-2016 · Cited by 6
Continuous Multimodal Emotion Recognition Approach for AVEC 2017Preprint
Singh, N., Singh, N., & Dhall, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.05861 · Cited by 4
Integrating Common Sense in Physically Embedded Intelligent Systems
Daoutis, M., Coradeschi, S., & Amy, L. (2009) · Ambient intelligence and smart environments · doi:10.3233/978-1-60750-034-6-212 · Cited by 4
Development of A Multi-Client Student Attendance Monitoring System
Jia, Y. P., Kwee, L., & Wong, H. L. (2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.c1002.1083s19 · Cited by 4
Identification of Illustrators
Şener, F., Samet, N., & Şahin, P. D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33863-2_61 · Cited by 4
Integrating spatial & spectral information for change detection in hyperspectral imagery
Vongsy, K. M., & Mendenhall, M. J. (2016) · Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers.2016.8071703 · Cited by 4
Optimized Transform Coding for Approximate KNN Search
Park, M., Gunda, K., Gupta, H., et al. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.34 · Cited by 4
Mastodon: the Command Center for Large-Scale Lineage-Tracing Microscopy DatasetsPreprint
Girstmair, J., Pietzsch, T., Ulman, V., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.10.693416 · Cited by 4
DynamicSurf: Dynamic Neural RGB-D Surface Reconstruction With an Optimizable Feature Grid
Mohamed, M., & Agapito, L. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00046 · Cited by 4
Global similarity preserving hashing
Liu, Y., Feng, L., Liu, S., et al. (2017) · Soft Computing · doi:10.1007/s00500-017-2683-7 · Cited by 4
Robust Face Recognition from Video based on Extensive Feature Set and Fuzzy_Bat Algorithm
Jana, S., & Kannan, N. (2015) · Indian Journal of Science and Technology · doi:10.17485/ijst/2015/v8i35/82202 · Cited by 4
Remote sensing image registration based on KICA-SIFT descriptors
Liu, X., Tian, Z., Leng, C., et al. (2010) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2010.5569671 · Cited by 4
What Did My Users Experience? Discovering Visual Stimuli on Graphical User Interfaces of the Web
Menges, R., Staab, S., Schaefer, C., Walber, T., & Kumar, C. (2025) · ACM Transactions on the Web · doi:10.1145/3715881 · Cited by 5
Improved SIFT Feature-Based Watermarking Method for IHC Ver. 5
Hayashi, M., & Kawamura, M. (2018) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659458 · Cited by 4
Probabilistic Handling of Merged Detections in Multi Target Tracking
Stephan, T., & Grinberg, M. (2012) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2012.56 · Cited by 4
Contour-based approach for 3D mapping of underwater galleries
Massone, Q., Druon, S., Breux, Y., et al. (2020) · Global Oceans 2020: Singapore – U.S. Gulf Coast · doi:10.1109/ieeeconf38699.2020.9388982 · Cited by 4
Automated Identification of Wood Surface Defects Based on Deep Learning
Syla, B., Saleh, S. M., & Hardt, W. (2023) · Embedded Selforganising Systems · doi:10.14464/ess.v10i7.627 · Cited by 5
Parallel spatial matching for object retrieval implemented on GPU
Wang, W., Zhang, D., Zhang, Y., et al. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5582930 · Cited by 4
Evaluating the impact of recovery density on augmented reality tracking
Coffin, C., Lee, C., & Höllerer, T. (2011) · IEEE International Symposium on Mixed and Augmented Reality (ISMAR) · doi:10.1109/ismar.2011.6092374 · Cited by 4
An automated registration of RS images based on SURF and piecewise linear transformation
Guo, H. N., Cheng, C., & Yang, Y. (2010) · doi:10.1109/esiat.2010.5568981 · Cited by 4
Metadata
- Authors
- Lowe, D. G.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Lowe, David G.
- ORCID
- Lowe, David G.
- Venue
- International Journal of Computer Vision
- Published
- 2004-11
- Volume
- 60
- Issue
- 2
- Pages
- 91-110
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2151103935
- Semantic Scholar
- 8c04f169203f9e55056a6f7f956695babe622a38
- Cited by
- 55,589
- References
- 50
- Citations by year
- 2026: 1480 · 2025: 2400 · 2024: 2519 · 2023: 2604 · 2022: 2705 · 2021: 3066 · 2020: 2933 · 2019: 3240 · 2018: 3232 · 2017: 3363 · 2016: 3431 · 2015: 3747 · 2014: 3569 · 2013: 3333 · 2012: 3003
- FWCI
- 699.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Keywords
- feature matching, affine invariance, rotation invariance, object recognition, nearest neighbor matching, Hough transform, pose estimation, occlusion
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review